Is the MCI a Useful Signal of Monetary Policy Conditions? An Empirical Investigation
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Bibliographic record
Abstract
This paper explores some of the potential advantages and disadvantages of monetary policy indicators based on a linear combination of a selected interest rate and the trade‐weighted exchange rate. The resulting measure, called a monetary conditions index (MCI), may provide a means to increase the transparency and credibility of monetary policy but it can also increase confusion among financial market participants if they view the central bank as reacting too closely to every ‘wiggle’ in the MCI. I argue that the aggregation of financial asset prices into an index can have salutary effects on the conduct of monetary policy because it can filter out some of the ‘noise’ in high frequency data. The danger comes from a central bank that stipulates following solely the MCI as a guide to the stance of monetary policy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it